class_docstr: This class implements a convolutional encoder to extract classification embeddings from logspectra.
  
  Arguments
  ---------
  dim : int
      Number of channels of the extracted embeddings.
  
  Returns
  --------
  Latent representations to feed inside classifier and/or intepreter.
  
  Example:
  --------
  >>> inputs = torch.ones(3, 431, 513)
  >>> model = Conv2dEncoder_v2()
  >>> print(model(inputs).shape)
  torch.Size([3, 256, 26, 32])
docstr: Computes forward pass.
  Arguments
  --------
  x : torch.Tensor
      Log-power spectrogram. Expected shape `torch.Size([B, T, F])`.
  
  Returns
  --------
  Embeddings : torch.Tensor
docstr_tok: ["Computes","forward","pass",".","Arguments","--","--","--","--","x",":","torch.Tensor","Log-power","spectrogram",".","Expected","shape","`","torch.Size","(","[","B",",","T",",","F","]",")","`",".","Returns","--","--","--","--","Embeddings",":","torch.Tensor"]
code: def forward(self, x):
      """
      Computes forward pass.
      Arguments
      --------
      x : torch.Tensor
          Log-power spectrogram. Expected shape `torch.Size([B, T, F])`.
  
      Returns
      --------
      Embeddings : torch.Tensor
  
      """
      x = x.unsqueeze(1)
      h1 = self.conv1(x)
      h1 = self.bn1(h1)
      h1 = self.nonl(h1)
  
      h2 = self.conv2(h1)
      h2 = self.bn2(h2)
      h2 = self.nonl(h2)
  
      h3 = self.conv3(h2)
      h3 = self.bn3(h3)
      h3 = self.nonl(h3)
  
      h4 = self.conv4(h3)
      h4 = self.bn4(h4)
      h4 = self.nonl(h4)
  
      h4 = self.resblock(h4)
  
      return h4
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
label_desc: Use image node_id 2 for calling the Conv2dEncoder_v2 obj's underlying member method code with example usage: obj.forward(x) and returns: h4